Commercial detection based on audio fingerprinting

ABSTRACT

A commercial detection system generates a probe audio fingerprint of an audio signal associated with a media stream on a broadcast channel and determines whether the media stream has commercial content based on analysis of the probe audio fingerprint. The commercial detection system determines whether a same match between the probe audio fingerprint and a reference audio fingerprint is observed across multiple broadcast channels. Responsive to the number of same matches exceeding a predetermined threshold, the commercial detection system determines that the media stream has commercial content. The commercial detection system may also apply a trained feature analysis model to extracted acoustic features of the audio signal. The commercial detection system determines whether the media stream has commercial content based on a confidence score assigned to the probe audio fingerprint. The commercial detection system reduces false positive detection using program guide information of the media stream.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 14/552,039, filed Nov. 24, 2014, which is incorporated by referencein its entirety.

BACKGROUND

This disclosure generally relates to content identification, and morespecifically to detecting commercials in media streams based on audiofingerprinting.

Commercial detection in media streams has become increasingly importantbecause many media streams, such as TV broadcasting, include commercialsbetween segments of media programs. A media stream can be an audiostream, a video stream or a combined audio and video stream (also called“audio-visual stream”). Commercials can appear in audio streams oraudio-visual streams, such as advertisements on broadcast radio andtelevision stations, and songs or video on music channels.

Existing content-based identification systems use various approaches,such as a feature-based approach and a recognition based approach todetect commercials in media streams. A feature-based approach typicallyuses some inherent characteristics of TV commercials to differentiatecommercials from non-commercial media content. For example, afeature-based approach may rely on the detection of scene changes invideo frames or the detection of black frames at the beginning and endof a TV commercial. A recognition based approach attempts to identifycommercials in a media stream using a database of known commercials.However, both approaches are computationally expensive and often requirelarge storage space.

Another method of content-based identification is audio fingerprinting.An audio fingerprint is a compact summary of an audio signal that can beused to perform content-based identification. For example, an audiofingerprint of an unidentified audio signal is compared to referenceaudio fingerprints for identification of the audio signal. Some existingsolutions of commercial detection using audio fingerprints of an audioportion of a media stream often generate an unnecessarily large numberof false positive identifications because the existing solutions fail todifferentiate commercial content from repeating real media content. Forexample, a signature tune of a particular TV program may repeat eachtime the particular TV program is aired. Thus, existing solutions ofcommercial detection using audio fingerprints fail to accurately detectcommercials in media streams.

SUMMARY

A commercial detection system generates a probe audio fingerprint of anaudio signal associated with a media stream on a broadcast channel anddetermines whether the media stream has commercial content based on ananalysis of the probe audio fingerprint. The commercial detection systemreduces false positive detection using program guide information of themedia stream and verifies the commercial detection across multiple mediastreams on different broadcast channels. For example, the commercialdetection system determines whether the same match is observed acrossmultiple broadcast programs and/or multiple broadcast channels over aspecified period of time. Responsive to the number of same matchesexceeding a predetermined threshold, the commercial detection systemdetermines that the media stream associated with the probe audiofingerprint contains commercial content.

Embodiments of the commercial detection system improve commercialdetection by supplementing the method above with a trained analysismodel and/or detection verification using program guide information. Inone embodiment, the commercial detection system extracts multipleacoustic features in a frequency domain and a time domain from the audiosignal associated with the media stream, e.g., spectral featurescomputed on the magnitude spectrum of the audio signal, Mel-frequencycepstral coefficients (MFCC) of the audio signal, a spectral bandwidthand spectral flatness measure of the audio signal, a spectralfluctuation, extreme value frequencies, and silent frequencies of theaudio signal. The system then applies a trained feature analysis modelto the extracted features of the audio signal. The feature analysismodel can be trained using one or more machine learning algorithms toanalyze selected acoustic features of audio signals. The commercialdetection system assigns a confidence score to the probe audiofingerprint based on the analysis of the acoustic features and theconfidence score indicates a likelihood that the media stream associatedwith the probe audio fingerprint has commercial content. A higherconfidence score associated with a media stream indicates that the mediastream is more likely to have commercial content than a media streamhaving a lower confidence score. The commercial detection systemdetermines whether the media stream has commercial content based on theconfidence score.

The features and advantages described in this summary and the followingdetailed description are not all-inclusive. Many additional features andadvantages will be apparent to one of ordinary skill in the art in viewof the drawings, specification, and claims hereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a commercial detection system in accordancewith an embodiment.

FIG. 2 is a block diagram of a system environment including a commercialdetection system in accordance with an embodiment.

FIG. 3 is a flowchart of detecting commercials in a media stream inaccordance with an embodiment.

FIG. 4 is a flowchart of detecting commercials in a media stream inaccordance with another embodiment.

FIG. 5 is a flowchart of detecting commercials in a media stream uponuser requests in accordance with an embodiment.

FIG. 6 is an example of commercial detection over multiple mediabroadcast channels in accordance with an embodiment.

The figures depict various embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the structures and methodsillustrated herein may be employed without departing from the principlesdescribed herein.

DETAILED DESCRIPTION

Overview

Embodiments of the invention enable commercial detection in mediastreams based on audio fingerprints. FIG. 1 shows an example embodimentof a commercial detection system 100 for detecting commercials in amedia stream (not shown) based on identification of audio portion of themedia stream, i.e., audio source 101. As shown in FIG. 1, the commercialdetection system 100 has a feature extraction module 110, a fingerprintgeneration module 120, a commercial detection module 130 and a programguide module 140. Additionally, the commercial detection system 100 hasa real-time matching database 150, a static matching database 152 and acommercial database 154 for storing various types of audio fingerprints.Some embodiments of the commercial detection system 100 do not includeall of these modules and/or databases or include different modulesand/or databases. The commercial detection system 100 receives an audiosignal 102 generated by an audio source 101, e.g., audio portion of TVbroadcast, extracts one or more acoustic features from the audio signal102 by the feature extraction module 110, generates an audio fingerprintof the audio signal 102 by the fingerprint generation module 120,detects commercials in the media stream by the commercial detectionmodule 130 and verifies the commercial detection by the program guidemodule 140.

As shown in FIG. 1, an audio source 101 generates the audio signal 102.The audio source 101 may be any entity suitable for generating audio (ora representation of audio), such as a person, an animal, speakers of amobile device, a desktop computer transmitting a data representation ofa song, or other suitable entity generating audio. The audio source 101can be the media streams provided by media content providers, such asbroadcast audio and video from radio and TV stations. The audio signal102 comprises one or more discrete audio frames, each of whichcorresponds to a fragment of the audio signal 102 at a particular time.Hence, each audio frame of the audio signal 102 corresponds to a lengthof time of the audio signal 102, such as 25 ms, 50 ms, 100 ms, 200 ms,etc.

In one embodiment, upon receiving the one or more audio frames of theaudio signal 102, the feature extraction module 110 extracts one or moreacoustic features from the audio signal 102. Examples of the acousticfeatures extracted from the audio signal 102 include acoustic featuresin frequency domain, such as spectral features computed on the magnitudespectrum of the audio signal 102, Mel-frequency cepstral coefficients(MFCC) of the audio signal 102, spectral bandwidth and spectral flatnessmeasure of the audio signal 102, a spectral fluctuation, extreme valuefrequencies, and silent frequencies of the audio signal 102. Someembodiments of the system 100 do not perform a feature extraction, andso may not include a feature extraction module 110.

The acoustic feature extracted from the audio signal 102 also includesacoustic features in temporal domain, such as the mean, standarddeviation and the covariance matrix of feature vectors over a texturewindow of the audio signal 102. Other embodiments of the featureextraction module 110 may include additional and/or different acousticfeatures extracted from the audio signal 102, such as volume changes ofthe audio signal 102 over a period of time and compression format of theaudio signal 102 if the audio signal 102 is compressed. The extractedacoustic features of the audio signal 102 can be used to train an audiofeature analysis model using machine learning. The commercial detectionmodule 130 can use the trained analysis model to enhance the accuracyand performance of commercial detection.

The fingerprint generation module 120 generates an audio fingerprintfrom one or more of the audio frames of the audio signal 102. Forsimplicity and clarity, the audio fingerprint of the audio signal 102 isreferred to as a “probe audio fingerprint” throughout the entiredescription. The probe audio fingerprint of the audio signal 102 mayinclude characteristic information describing the audio signal 102. Suchcharacteristic information may indicate acoustical and/or perceptualproperties of the audio signal 102. In one embodiment, the fingerprintgeneration module 120 preprocesses the audio signal 102, transforms theaudio signal 102 from one domain to another domain, filters thetransformed audio signal and generates the audio fingerprint from thefurther transformed audio signal. One example of the fingerprintgeneration module 120 is discrete cosine transform (DCT) based audiofingerprint generation described in U.S. application Ser. No.14/153,404, which is incorporated by reference herein in its entirety.

The commercial detection module 130 detects commercials in a mediastream based on the audio fingerprints associated with the audio signalsof the media stream. To detect commercials based on the probe audiofingerprint of the audio signal 102, the commercial detection module 130matches the probe audio fingerprint of the audio signal 102 againstreference audio fingerprints stored in at least one of the real-timematching database 150, the static matching database 152 and thecommercial database 154. Based on the comparison and a predeterminedthreshold, the commercial detection module 130 determines whethercommercials are present in the media stream. Alternatively, thecommercial detection module 130 applies a trained analysis module to theacoustic features associated with the media stream for commercialdetection. Embodiments of the commercial detection module 130 arefurther described below with references to FIGS. 3-6.

The program guide module 140 interacts with the commercial detectionmodule 130 to verify the commercial detection results. A same commercialcan repeat on a same broadcast channel and on other broadcast channelsat various times, e.g., a few minutes later or months later. Similarly,real media content (e.g., commercial-free media content) can also repeaton the same or different broadcast channels. For example, a signaturetune of a particular TV program (e.g., “Friends”) may repeat each timethe particular TV program is aired. An audio fingerprint for thesignature tune can be found across multiple broadcast channels atdifferent times (e.g., many TV stations broadcast “Friends” overdifferent TV channels at different times). The commercial detectionmodule 130 may false positively identify the audio fingerprint for thesignature tune as a commercial because the number of repetitions of thesignature tune across multiple broadcast channels has exceeded apredetermined threshold. The program guide module 140 can reduce falsepositive detection using program guide information for media streams.

In one embodiment, the program guide module 140 has a local storage tostore program guide information (e.g., scheduling information) for knownTV shows for TV broadcast channels and for songs and playlists for musicchannels and audio stations. The program guide information for a givenshow may be provided by the content provider of the given show and theprogram guide information includes starting and ending times for eachcommercial to be broadcast with the given show. Using the TV show“Friends” as an example, the broadcast station for the show may providea program guide that indicates that after every 10 minutes of the show,there is a TV advertisement for 10 seconds.

The program guide module 140 verifies the detection using program guideinformation to reduce false positive detection. For example, if thesignature tune of “Friends” appears on a broadcast channel at a timeslot, which matches the time slot scheduled for “Friends” for thebroadcast channel, the program guide module 140 can flag theidentification as a potential false positive identification. It canfurther compare the audio fingerprint of the signature tune with areference fingerprint for the signature tune and evaluate the detectionbased on the comparison.

Reference audio fingerprints are stored in various audio fingerprintsdatabases of the commercial detection system 100. The real-time matchingdatabase 150 stores audio fingerprints generated for audio signalsreceived by the commercial detection system 100, such as the probe audiofingerprint for the audio signal 102. In one embodiment, the real-timematching database 150 stores audio fingerprints for audio signalssampled from media streams, e.g., TV talk shows and movies, broadcastedover two hundred broadcast channels in a seven-day time window. Theaudio fingerprints stored in the real-time matching database 150 areupdated periodically (e.g., daily) or upon request. The number ofbroadcast channels and size of the time windows are configurable designparameters. Different embodiments of the real-time matching database 150can store audio fingerprints from a different number of broadcastchannels and/or different time window sizes.

A match between the probe audio fingerprint of the audio signal 102 anda reference fingerprint stored in the real-time matching database 150indicates that the media stream associated with the audio signal 102 mayhave commercial content. The detection of possible commercial contentbased on the match is not deterministic, i.e., the detection may be afalse positive. For example, a match between a probe audio fingerprintof a song and a reference audio fingerprint stored in the real-timematching database 150 may not positively identify the media contentassociated with the probe audio fingerprint as a commercial because thesong can be a sound track for an advertisement or a real song broadcastby a radio station.

The static matching database 152 stores reference audio fingerprintsgenerated for audio signals received by the commercial detection system100, where the audio fingerprints are associated with media content ofmedia streams (e.g., TV shows) and have been verified ascommercial-free. In one embodiment, the audio fingerprints stored in thestatic matching database 152 are the audio fingerprints generated by thefingerprint generation module 120, where the audio fingerprints havebeen verified by both the commercial detection module 130 and theprogram guide module 140.

A match between the probe audio fingerprint of the audio signal 102 anda reference fingerprint stored in the static matching database 152indicates that the media content of the media stream associated with theaudio signal 102 is commercial free. It is noted that a match betweenthe probe audio fingerprint of the audio signal 102 and a referencefingerprint for an audio stream (e.g., music songs) stored in the staticmatching database 152 may not affirmatively indicate that the audiostream has no commercial content because the audio stream may be thesound track for a commercial consisting of only audio data. In suchscenario, the program guide module 140 can further investigate thematch.

The commercial database 154 stores reference audio fingerprintsgenerated for audio signals received by the commercial detection system100, where the audio fingerprints are associated with media content thathas been verified to be a commercial. In one embodiment, the commercialdatabase 154 stores audio fingerprints for known commercials that wererecognized by human in advance. In another embodiment, the commercialdatabase 154 stores audio fingerprints of commercials detected by thecommercial detection system 100 in real time. If a match between theprobe audio fingerprint of the audio signal 102 and a referencefingerprint stored in the commercial database 154 is found, the mediastream associated with the probe audio fingerprint is determined to havecommercial content.

In addition to storing the reference audio fingerprints, each databasedescribed above, the real-time matching database 150, the staticmatching database 152 and the commercial database 154, may storeidentifying information and/or other information related to the audiosignals from which the reference audio fingerprints were generated. Theidentifying information may be any data suitable for identifying anaudio signal. For example, the identifying information associated with areference audio fingerprint includes title, artist, album, publisherinformation for the corresponding audio signal. Identifying informationmay also include data indicating the source of an audio signalcorresponding to a reference audio fingerprint. For example, thereference audio signal of an audio-based advertisement may be broadcastfrom a specific geographic location, so a reference audio fingerprintcorresponding to the reference audio signal is associated with anidentifier indicating the geographic location (e.g., a location name,global positioning system (GPS) coordinates, etc.). The commercialdatabase 154 may include a confidence level for a reference audiofingerprint, which can be updated time from time (increase or decreasedepending on the new available information).

System Architecture

FIG. 2 is a block diagram illustrating one embodiment of a systemenvironment 200 including a commercial detection system 100. As shown inFIG. 2, the system environment 200 includes one or more client devices202, one or more external systems 203, the commercial detection system100 and a social networking system 205 connected through a network 204.While FIG. 2 shows three client devices 202, one social networkingsystem 205, and one external system 203, it should be appreciated thatany number of these entities (including millions) may be included. Inalternative configurations, different and/or additional entities mayalso be included in the system environment 200. Furthermore, in someembodiments, the commercial detection system 100 can be a system ormodule running on or otherwise included within one of the other entitiesshown in FIG. 2.

A client device 202 is a computing device capable of receiving userinput, as well as transmitting and/or receiving data via the network204. In one embodiment, a client device 202 sends a request to thecommercial detection system 100 to identify an audio signal captured orotherwise obtained by the client device 202. The client device 202 mayadditionally provide the audio signal or a digital representation of theaudio signal to the commercial detection system 100. Examples of clientdevices 202 include desktop computers, laptop computers, tabletcomputers (pads), mobile phones, personal digital assistants (PDAs),gaming devices, or any other device including computing functionalityand data communication capabilities. Hence, the client devices 202enable users to access the commercial detection system 100, the socialnetworking system 205, and/or one or more external systems 203. In oneembodiment, the client devices 202 also allow various users tocommunicate with one another via the social networking system 205.

The network 204 may be any wired or wireless local area network (LAN)and/or wide area network (WAN), such as an intranet, an extranet, or theInternet. The network 204 provides communication capabilities betweenone or more client devices 202, the commercial detection system 100, thesocial networking system 205, and/or one or more external systems 203.In various embodiments the network 204 uses standard communicationtechnologies and/or protocols. Examples of technologies used by thenetwork 204 include Ethernet, 802.11, 3G, 4G, 802.16, or any othersuitable communication technology. The network 204 may use wireless,wired, or a combination of wireless and wired communicationtechnologies. Examples of protocols used by the network 204 includetransmission control protocol/Internet protocol (TCP/IP), hypertexttransport protocol (HTTP), simple mail transfer protocol (SMTP), filetransfer protocol (TCP), or any other suitable communication protocol.

The external system 203 is coupled to the network 204 to communicatewith the commercial detection system 100, the social networking system205, and/or with one or more client devices 202. The external system 203provides content and/or other information to one or more client devices202, the social networking system 205, and/or to the commercialdetection system 100. Examples of content and/or other informationprovided by the external system 203 include identifying informationassociated with reference audio fingerprints, content (e.g., audio,video, etc.) associated with identifying information, or other suitableinformation.

The social networking system 205 is coupled to the network 204 tocommunicate with the commercial detection system 100, the externalsystem 203, and/or with one or more client devices 202. The socialnetworking system 205 is a computing system allowing its users tocommunicate, or to otherwise interact, with each other and to accesscontent. The social networking system 205 additionally permits users toestablish connections (e.g., friendship type relationships, followertype relationships, etc.) between one another. Though the socialnetworking system 205 is included in the embodiment of FIG. 2, thecommercial detection system 100 can operate in environments that do notinclude a social networking system, including within any environment forwhich commercial detection in media streams is desirable. Further, insome embodiments, the commercial detection system 100 is a component ofthe social networking system such that the social networking systemperforms the functions of the commercial detection system 100.

In one embodiment, the social networking system 205 stores user accountsdescribing its users. User profiles are associated with the useraccounts and include information describing the users, such asdemographic data (e.g., gender information), biographic data (e.g.,interest information), etc. Using information in the user profiles,connections between users, and any other suitable information, thesocial networking system 205 maintains a social graph of nodesinterconnected by edges. Each node in the social graph represents anobject associated with the social networking system 205 that may act onand/or be acted upon by another object associated with the socialnetworking system 205. An edge between two nodes in the social graphrepresents a particular kind of connection between the two nodes. Forexample, an edge may indicate that a particular user of the socialnetworking system 205 is currently “listening” to a certain song. In oneembodiment, the social networking system 205 may use edges to generatestories describing actions performed by users, which are communicated toone or more additional users connected to the users through the socialnetworking system 205. For example, the social networking system 205 maypresent a story about a user listening to a song to additional usersconnected to the user. A user listening to a song can record a portionof the audio with a client device 202 and the audio can be provided tothe social networking system 205 that can then identify the audio ascorresponding to a particular song, and can provide a story to one ormore of the user's connections that the user is listening to the song.The commercial detection system 100 to can be used by the socialnetworking system 205 in this identification process to determinewhether the audio recording is a commercial and to ensure the audiorecording is correctly identified for inclusion in the story.

Commercial Detection Based on Audio Fingerprinting

The commercial detection module 130 detects commercials in a mediastream based on the audio fingerprints associated with the audio signalsof the media stream. The program guide module 140 verifies the detectionresults from the commercial detection module 130. To detect commercialsbased on the probe audio fingerprint of the audio signal 102, thecommercial detection module 130 matches the probe audio fingerprint ofthe audio signal 102 against reference audio fingerprints stored in atleast one of the real-time matching database 150, the static matchingdatabase 152 and the commercial database 154. Based on the comparison,the commercial detection module 130 determines whether commercialcontent is present in the media stream.

To match the probe audio fingerprint with a reference audio fingerprint,the commercial detection module 120 calculates a correlation between theprobe audio fingerprint and the reference audio fingerprint. Thecorrelation measures the similarity between the acoustic characteristicsof the probe audio fingerprint and the acoustic characteristics of thereference audio fingerprint. In one embodiment, the commercial detectionmodule 130 measures the similarity between the acoustic characteristicsof the probe audio fingerprint and the acoustic characteristics of thereference audio fingerprint based on DCT sign-only correction betweenthe probe audio fingerprint and the reference audio fingerprint. It isnoted that DCT sign-only correlation between the probe audio fingerprintand the reference audio fingerprint closely approximates the similaritybetween the acoustic characteristics of the probe audio fingerprint andthe audio characteristics of the reference audio fingerprint. Examplesof the DCT sign-only correlation are described in U.S. application Ser.No. 14/153,404, which is incorporated by reference herein in itsentirety.

A same commercial can repeat on a same broadcast channel and on otherbroadcast channels at various times. A single match between the probeaudio fingerprint and a reference audio fingerprint in the real-timematching database 150 may not affirmatively indicate that the mediastream associated with the probe audio fingerprint has commercialcontent. In one embodiment, the commercial detection module 130determines whether the same match is observed across multiple broadcastchannels over a specified period of time. Responsive to the number ofsame matches exceeding a predetermined threshold, the commercialdetection module 130 determines that the media stream associated withthe probe audio fingerprint has commercial content. Examples of thepredetermined threshold include twenty matches over five broadcastchannels and ten matches in three different broadcast programs.

Turning now to FIG. 6, an example is shown illustrating commercialdetection over multiple media broadcast channels in accordance with anembodiment. The example in FIG. 6 shows a program guide database 610that stores program guide information (e.g., scheduling information) forknown TV shows for TV broadcast channels and for songs and playlists formusic channels and audio stations. In one embodiment, the program guidedatabase 610 stores three different broadcast programs (e.g., threedifferent TV shows), Program A 620, Program B 630 and Program C 640. Thecommercial detection system 100 generates audio fingerprints for audiosignals of the three broadcast programs, e.g., audio fingerprint 650Afor an audio signal associated with Program A, audio fingerprint 650Bfor an audio signal associated with Program B and audio fingerprint 650Cfor an audio signal associated with Program C. The audio fingerprint605A corresponds to the audio content of Program A at time t1; the audiofingerprint 605B corresponds to the audio content of Program B at timet2; and the audio fingerprint 605C corresponds to the audio content ofProgram C at time t3. The timing parameters, t1, t2 and t3, representdifferent time slots when the audio content of Program A, Program B andProgram C, respectively, is played.

The commercial detection module 130 matches the audio fingerprint 650Awith the reference audio fingerprints stored in the real-time matchingdatabase 150 and finds a match. For example, audio fingerprints 650B and650C could be reference audio fingerprints with which probe audiofingerprint 650A might be matched. To confirm that Program A indeedcontains commercial content, the commercial detection module 130 checkswhether the same match is observed over Program B and Program C. Forexample, if probe audio fingerprint 650A is found to match referenceaudio fingerprints 650B and 650C, since these are fingerprints acrossvarious different programs (e.g., different TV shows), it is likely thatthese are commercials. Multiple different programs are unlikely to havethe same content displayed during the program other than a commercial.Assuming that the commercial detection module 130 finds two more matchesover Program B and Program C, and that the predetermined threshold isthree matches over three different broadcast programs, the commercialdetection module 130 determines that Program A has commercial content,and a summary of the audio characteristics of the commercial content isrepresented by the audio fingerprint 650A. Thus, in this embodiment, thecommercial detection module 130 compares a probe fingerprint from oneprogram to reference fingerprints taken from other programs. If there isa match by the probe fingerprint to some threshold number of referencefingerprints across some threshold number of different programs, thenthe probe fingerprint is determined to be a fingerprint from acommercial.

This probe fingerprint can then be stored in the commercial database154. In this manner, the commercial detection system 100 can build arich database of commercial fingerprints. This rich commercial database154 can then be used to identify commercials in new audio signals thatare received for which fingerprints are generated (or new fingerprintsare received). For example, a user might record audio with a clientdevice and the commercial detection system 100 can generate afingerprint of that audio and compare it to the commercial database 154to determine if there is a match. If so, the user is determined to belistening to a commercial. Where the user is recording audio forconveying a story to connections on a social networking system, thesocial networking system can use this information to provide a storythat correctly identifies the audio being listened to by the user.

FIG. 3 is a flowchart illustrating a method of detecting commercials ina media stream by the commercial detection system 100 in accordance withan embodiment. This method can be used, for example, to build thedatabases illustrated in FIG. 1, including building a rich commercialdatabase 154 as explained above. Initially, the commercial detectionsystem 100 receives 310 an audio signal associated with the media streamand the commercial detection system 100 generates 320 a probe audiofingerprint for the audio signal. The commercial detection system 100compares 330 the probe audio fingerprint with reference audiofingerprints stored in the real-time matching database 150 to find anymatch. Based on the comparison, the commercial detection system 100detects 340 whether there is commercial content in the media streamassociated with the probe audio fingerprint.

In one embodiment as illustrated in FIG. 6, responsive to a match foundbetween the probe audio fingerprint and a reference audio fingerprint inthe real-time matching database 150, the commercial detection system 100determines whether the same match is observed over multiple broadcastchannels. If the number of the matches exceeds a predetermined thresholdvalue, the commercial detection system 100 determines that the mediaprogram associated with the probe audio fingerprint has commercialcontent.

In response to the determination that the media stream associated withthe probe audio fingerprint includes commercial content, the commercialdetection system 100 stores 350 the probe audio fingerprint in thecommercial database 154. If the commercial detection system 100determines that the media program does not include commercial content,the commercial detection module stores 360 the probe audio fingerprintin the static matching database 152.

To increase accuracy of commercial detection based on audiofingerprinting, the commercial detection system 100 applies a trainedanalysis model to acoustic features of media streams. In one embodiment,the commercial detection module 130 trains the analysis model using oneor more machine learning algorithms to analyze selected acousticfeatures of audio signals. Machine learning techniques and algorithmsinclude, but are not limited to, neural networks, naïve Bayes, supportvector machines and machine learning used in Hive frameworks.

The commercial detection system 100 receives audio signals from mediacontent provided by content providers and extracts acoustic features,e.g., MFCC and standard deviation of feature vectors, from the audiosignals. The extracted acoustic features are clustered and classified bythe analysis model using one or more machine learning algorithms. Thecommercial detection system 100 generates audio fingerprints of audiosignals corresponding to the classified acoustic features. For example,the commercial detection system 100 can select a set of acousticfeatures of known commercials and generates signature audio fingerprintsof the known commercials based on the selected acoustic features.

When presented with a probe audio fingerprint, the trained analysismodel compares the acoustic features summarized by the probe audiofingerprint with the classified acoustic features summarized bysignature audio fingerprints of known commercials. Based on theanalysis, the trained analysis model determines the degree ofsimilarities between the acoustic features associated with the probeaudio fingerprint and the acoustic features of a signature audiofingerprint. A confidence score can be assigned to the probe audiofingerprint for each comparison and the confidence score indicates alikelihood that the media stream associated with the probe audiofingerprint has commercial content. A higher confidence score associatedwith a media stream indicates that the media stream is more likely tohave commercial content than a media stream having a lower confidencescore.

FIG. 4 is a flowchart illustrating a method of detecting commercials ina media stream by the commercial detection system 100 in accordance withanother embodiment. As with FIG. 3, this method of FIG. 4 can be used,for example, to build the databases illustrated in FIG. 1, includingbuilding a rich commercial database 154 as explained above. However,with FIG. 4, the prediction of whether or not a fingerprint represents acommercial is bolstered using the trained analysis model describedabove. Initially, the commercial detection system 100 receives 410 anaudio signal associated with the media stream and the commercialdetection system 100 extracts 420 acoustic features from the audiosignal. The commercial detection system 100 applies 430 a trained audiofeature analysis model as described above to the extracted features. Thetrained audio feature analysis model compares the extracted acousticfeatures of the audio signal with classified acoustic features of knowncommercials. Based on the analysis, the commercial detection system 100determines 440 a confidence score for the probe audio fingerprintgenerated from the audio signal. The commercial detection system 100detects 450 commercial content in the media stream based on theconfidence score.

To further improve commercial detection accuracy, the commercialdetection system 100 can verify 460 the detection using program guideinformation as described above. Responsive to the media stream havingcommercial content 470, the commercial detection system 100 stores 480the audio fingerprint of detected commercial content in a commercialfingerprint database, e.g., the commercial database 154 in FIG. 1.Otherwise, the commercial detection system 100 analyzes another audiosignal following the steps 410 to 470.

Compared with conventional commercial detection techniques, commercialdetection based on audio fingerprinting described above improvecommercial detection performance by considering characteristics ofrepeating commercial across multiple broadcast channels and by reducingfalse positive detection with a trained analysis model and detectionverification using program guide information.

Applications of Commercial Detection Based on Audio Fingerprinting

Commercial detection based on audio fingerprinting has a variety ofapplications, such as for commercial detection in a video stream uponuser requests. The identified commercials and their correspondinglocations in the media streams help users to skip unwanted commercials.FIG. 5 is a flowchart illustrating a method of detecting commercials ina media stream by the commercial detection system 100 upon a userrequest in accordance with an embodiment. FIG. 5 thus illustrates theapplication of the databases that were built using FIG. 3 or 4 forcommercial detection. The user request could come, for example, directlyfrom a user or could be a user request submitted by a social networkingsystem based on audio received by the social networking system from theuser. Initially, the commercial detection system 100 receives 510 a userrequest for detection in a media stream, wherein the request identifiesthe audio portion of the media stream. For example, the user may haverecorded some audio with a mobile phone for which the user is requestingan identification to provide a story to the user's social networkingconnections about what the user is listening to. In one embodiment, theaudio portion of the media stream is compactly presented by an audiofingerprint, which is included in the user request. The commercialdetection system 100 retrieves 520 the audio fingerprint from the userrequest. Alternatively, the commercial detection system 100 generates anaudio fingerprint by the fingerprint generation module 120 for the audiosignals of the media stream.

The commercial detection system 100 compares 530 the audio fingerprintof the media stream with reference audio fingerprints in a commercialcontent database, e.g., the commercial database 154 in FIG. 1. Thereference audio fingerprints stored in the commercial content databaseare audio fingerprints for media content that has been verified by thecommercial detection system 100 as commercial content.

Responsive to finding a match 540 between the audio fingerprint of themedia stream with a reference audio fingerprint in the commercialcontent database, the commercial detection system 550 sends 550 aresponse to the user request, where the response indicates that themedia stream contains commercial content and information describing thedetected commercial, e.g., location/timing in the media stream andlength of the commercial.

Responsive to no match being found, the commercial detection system 100can further verify the detection by comparing 560 the audio fingerprintof the media stream with reference audio fingerprints stored in thereal-time matching database (e.g., the database 150 in FIG. 1) and/oraudio fingerprints stored in the static matching database (e.g., thedatabase 152 in FIG. 1). The commercial detection system 100 detects 570commercial content in the media stream based on the comparison asdescribed above. The commercial system 100 generates 580 a response tothe user request and sends 550 the response to the user request. Theresponse to the user request sent 550 can also be a response to a socialnetworking system request to identify audio provided by a user.

In addition to commercial detection upon user requests as discussedabove, the commercial detection based on audio fingerprinting can alsobe used in commercial detection in speech recognition, closed captionand other content-identification applications. The commercial detectionsystem 100 may observe user interactions with the detected commercialsand remove commercial content from media streams. The commercialdetection results and observed user interactions with the detectedcommercials can help content providers and social networking platformsto customize services provided to users and to enhance user experiencewith media streams. For example, the commercial detection system 100 mayinteract with the social networking system 205 illustrated in FIG. 2upon a user click of a detected commercial in a media stream, where thesocial networking system 205, in turn, links the detected commercial tocommercials to be presented at the user's social media account.

General

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may include ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Embodiments of the invention may also relate to a computer data signalembodied in a carrier wave, where the computer data signal includes anyembodiment of a computer program product or other data combinationdescribed herein. The computer data signal is a product that ispresented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating a probe audio fingerprint based on one or more of a pluralityof frames of an audio signal, the audio signal associated with a mediastream on a broadcast channel; and responsive to a match between theprobe audio fingerprint with a reference audio fingerprint of aplurality of reference audio fingerprints: determining whether an audiofingerprint associated with a media stream of a plurality of other mediastreams matches the reference audio fingerprint; determining that themedia stream associated with the probe audio fingerprint has commercialcontent in response to the number of matches between audio fingerprintsassociated with the plurality of other media streams and the referenceaudio fingerprint exceeding a predetermined threshold, wherein thereference audio fingerprint of the plurality of reference audiofingerprints represents a set of acoustic features of an audio signalassociated with commercial content.
 2. The computer-implemented methodof claim 1, wherein determining the media stream associated with theprobe audio fingerprint has commercial content further comprises:determining whether the match between the probe audio fingerprint andthe reference audio fingerprint of the plurality of referencefingerprints is present over a plurality of other broadcast channels. 3.The computer-implemented method of claim 2, further comprising:responsive to the number of matches between the probe audio fingerprintand the reference audio fingerprint present over the plurality of otherbroadcast channels exceeding a predetermined threshold, determining thatthe media stream associated with the probe audio fingerprint hascommercial content.
 4. The computer-implemented method of claim 1,further comprising: receiving program guide information associated withthe media stream on the broadcast channel, the program guide informationdescribing scheduling information of known commercials broadcast alongwith the media stream on the broadcast channel; verifying the matchbetween the probe audio fingerprint and the reference audio fingerprintof a plurality of reference audio fingerprints based on the programguide information associated with the media stream; and confirming thatthe media stream associated with the probe audio fingerprint hascommercial content based on the verification of the match.
 5. Thecomputer-implemented method of claim 1, further comprising: receivingprogram guide information associated with the plurality of other mediastreams, the program guide information describing scheduling informationof known commercials broadcast along with the other media streams;verifying the match between the audio fingerprint associated with amedia stream of the plurality of other media streams and the referenceaudio fingerprint based on the program guide information associated withthe plurality of other media streams; and confirming that the mediastream associated with the probe audio fingerprint has commercialcontent based on the verification of the match.
 6. Thecomputer-implemented method of claim 1, further comprising: applying atrained analysis model to a plurality of acoustic features of the audiosignal associated with the media stream; and detecting commercial in themedia stream based on the application of the trained analysis model tothe plurality of acoustic features of the audio signal.
 7. Thecomputer-implemented method of claim 6, wherein the plurality ofacoustic features of the audio signal comprise a plurality of acousticfeatures of the audio signal in frequency domain, comprising: magnitudespectrum of the audio signal; Mel-frequency cepstral coefficients of theaudios signal; spectral bandwidth of the audio signal; spectral flatnessmeasure of the audio signal; spectral fluctuation of the audio signal;number of extreme value frequencies; and number of silent frequencies.8. The computer-implemented method of claim 6, wherein the plurality ofacoustic features of the audio signal comprise a plurality of acousticfeatures of the audio signal in time domain, comprising mean, standarddeviation and covariance matrix of features vectors of the audio signal,and the feature vectors of the audio signal being observed over apredefined time window.
 9. The computer-implemented method of claim 6,further comprising: determining a degree of similarities between theacoustic features associated with the probe audio fingerprint and aplurality of acoustic features of reference audio fingerprints of knowncommercial content; and assigning a confidence score to the probe audiosignal based on the determined degree of similarities, the confidencescore assigned to the probe audio fingerprint indicating a likelihoodthat the media stream associated with the probe audio fingerprint hascommercial content.
 10. The computer-implemented method of claim 1,further comprising: calculating a correlation between the probe audiofingerprint associate with the media stream and the reference audiofingerprint of the plurality of the reference audio fingerprints, thecorrelation measuring the similarity between acoustic characteristics ofthe probe audio fingerprint and acoustic characteristics of thereference audio fingerprint.
 11. A non-transitory computer-readablestorage medium storing computer program instructions executed by acomputer processor, the computer program instructions comprisinginstructions for: generating a probe audio fingerprint based on one ormore of a plurality of frames of an audio signal, the audio signalassociated with a media stream on a broadcast channel; and responsive toa match between the probe audio fingerprint with a reference audiofingerprint of a plurality of reference audio fingerprints: determiningwhether an audio fingerprint associated with a media stream of aplurality of other media streams matches the reference audiofingerprint; determining that the media stream associated with the probeaudio fingerprint has commercial content in response to the number ofmatches between audio fingerprints associated with the plurality ofother media streams and the reference audio fingerprint exceeding apredetermined threshold, wherein the reference audio fingerprint of theplurality of reference audio fingerprints represents a set of acousticfeatures of an audio signal associated with commercial content.
 12. Thecomputer-readable storage medium of claim 11, wherein the computerprogram instructions for determining the media stream associated withthe probe audio fingerprint has commercial content further comprisecomputer program instructions for: determining whether the match betweenthe probe audio fingerprint and the reference audio fingerprint of theplurality of reference fingerprints is present over a plurality of otherbroadcast channels.
 13. The computer-readable storage medium of claim12, further comprising computer program instructions for: responsive tothe number of matches between the probe audio fingerprint and thereference audio fingerprint present over the plurality of otherbroadcast channels exceeding a predetermined threshold, determining thatthe media stream associated with the probe audio fingerprint hascommercial content.
 14. The computer-readable storage medium of claim11, further comprising computer program instructions for: receivingprogram guide information associated with the media stream on thebroadcast channel, the program guide information describing schedulinginformation of known commercials broadcast along with the media streamon the broadcast channel; verifying the match between the probe audiofingerprint and the reference audio fingerprint of a plurality ofreference audio fingerprints based on the program guide informationassociated with the media stream; and confirming that the media streamassociated with the probe audio fingerprint has commercial content basedon the verification of the match.
 15. The computer-readable storagemedium of claim 11, further comprising computer program instructionsfor: receiving program guide information associated with the pluralityof other media streams, the program guide information describingscheduling information of known commercials broadcast along with theother media streams; verifying the match between the audio fingerprintassociated with a media stream of the plurality of other media streamsand the reference audio fingerprint based on the program guideinformation associated with the plurality of other media streams; andconfirming that the media stream associated with the probe audiofingerprint has commercial content based on the verification of thematch.
 16. The computer-readable storage medium of claim 11, furthercomprising computer program instructions for: applying a trainedanalysis model to a plurality of acoustic features of the audio signalassociated with the media stream; and detecting commercial in the mediastream based on the application of the trained analysis model to theplurality of acoustic features of the audio signal.
 17. Thecomputer-readable storage medium of claim 16, wherein the plurality ofacoustic features of the audio signal comprise a plurality of acousticfeatures of the audio signal in frequency domain, comprising: magnitudespectrum of the audio signal; Mel-frequency cepstral coefficients of theaudios signal; spectral bandwidth of the audio signal; spectral flatnessmeasure of the audio signal; spectral fluctuation of the audio signal;number of extreme value frequencies; and number of silent frequencies.18. The computer-readable storage medium of claim 16, wherein theplurality of acoustic features of the audio signal comprise a pluralityof acoustic features of the audio signal in time domain, comprisingmean, standard deviation and covariance matrix of features vectors ofthe audio signal, and the feature vectors of the audio signal beingobserved over a predefined time window.
 19. The computer-readablestorage medium of claim 16, further comprising computer programinstructions for: determining a degree of similarities between theacoustic features associated with the probe audio fingerprint and aplurality of acoustic features of reference audio fingerprints of knowncommercial content; and assigning a confidence score to the probe audiosignal based on the determined degree of similarities, the confidencescore assigned to the probe audio fingerprint indicating a likelihoodthat the media stream associated with the probe audio fingerprint hascommercial content.
 20. The computer-readable storage medium of claim11, further comprising computer program instructions for: calculating acorrelation between the probe audio fingerprint associate with the mediastream and the reference audio fingerprint of the plurality of thereference audio fingerprints, the correlation measuring the similaritybetween acoustic characteristics of the probe audio fingerprint andacoustic characteristics of the reference audio fingerprint.